Overview

Dataset statistics

Number of variables14
Number of observations3333
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory364.7 KiB
Average record size in memory112.0 B

Variable types

Numeric12
Categorical2

Alerts

day_charge is highly correlated with total_chargeHigh correlation
total_charge is highly correlated with day_charge and 1 other fieldsHigh correlation
churn is highly correlated with total_chargeHigh correlation
voice_mail_messages has 2411 (72.3%) zeros Zeros
customer_service_calls has 697 (20.9%) zeros Zeros

Reproduction

Analysis started2022-11-05 17:25:44.926830
Analysis finished2022-11-05 17:26:41.928495
Duration57 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

account_length
Real number (ℝ≥0)

Distinct212
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.0648065
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-05T22:56:42.270514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q174
median101
Q3127
95-th percentile167
Maximum243
Range242
Interquartile range (IQR)53

Descriptive statistics

Standard deviation39.82210593
Coefficient of variation (CV)0.3940254508
Kurtosis-0.1078359806
Mean101.0648065
Median Absolute Deviation (MAD)27
Skewness0.09660629423
Sum336849
Variance1585.800121
MonotonicityNot monotonic
2022-11-05T22:56:42.697546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10543
 
1.3%
8742
 
1.3%
10140
 
1.2%
9340
 
1.2%
9039
 
1.2%
9538
 
1.1%
8638
 
1.1%
10037
 
1.1%
11637
 
1.1%
11236
 
1.1%
Other values (202)2943
88.3%
ValueCountFrequency (%)
18
0.2%
21
 
< 0.1%
35
0.2%
41
 
< 0.1%
51
 
< 0.1%
62
 
0.1%
72
 
0.1%
81
 
< 0.1%
93
 
0.1%
103
 
0.1%
ValueCountFrequency (%)
2431
 
< 0.1%
2321
 
< 0.1%
2252
0.1%
2242
0.1%
2211
 
< 0.1%
2172
0.1%
2151
 
< 0.1%
2122
0.1%
2102
0.1%
2093
0.1%

voice_mail_messages
Real number (ℝ≥0)

ZEROS

Distinct46
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.099009901
Minimum0
Maximum51
Zeros2411
Zeros (%)72.3%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-05T22:56:43.177581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile36
Maximum51
Range51
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.68836537
Coefficient of variation (CV)1.690128243
Kurtosis-0.05112853879
Mean8.099009901
Median Absolute Deviation (MAD)0
Skewness1.264823634
Sum26994
Variance187.3713466
MonotonicityNot monotonic
2022-11-05T22:56:43.751131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
02411
72.3%
3160
 
1.8%
2953
 
1.6%
2851
 
1.5%
3346
 
1.4%
2744
 
1.3%
3044
 
1.3%
2442
 
1.3%
2641
 
1.2%
3241
 
1.2%
Other values (36)500
 
15.0%
ValueCountFrequency (%)
02411
72.3%
41
 
< 0.1%
82
 
0.1%
92
 
0.1%
101
 
< 0.1%
112
 
0.1%
126
 
0.2%
134
 
0.1%
147
 
0.2%
159
 
0.3%
ValueCountFrequency (%)
511
 
< 0.1%
502
 
0.1%
491
 
< 0.1%
482
 
0.1%
473
 
0.1%
464
 
0.1%
456
 
0.2%
447
0.2%
439
0.3%
4215
0.5%

customer_service_calls
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.562856286
Minimum0
Maximum9
Zeros697
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-05T22:56:44.122158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.315491045
Coefficient of variation (CV)0.8417223368
Kurtosis1.730913655
Mean1.562856286
Median Absolute Deviation (MAD)1
Skewness1.091359482
Sum5209
Variance1.730516689
MonotonicityNot monotonic
2022-11-05T22:56:44.432180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
11181
35.4%
2759
22.8%
0697
20.9%
3429
 
12.9%
4166
 
5.0%
566
 
2.0%
622
 
0.7%
79
 
0.3%
92
 
0.1%
82
 
0.1%
ValueCountFrequency (%)
0697
20.9%
11181
35.4%
2759
22.8%
3429
 
12.9%
4166
 
5.0%
566
 
2.0%
622
 
0.7%
79
 
0.3%
82
 
0.1%
92
 
0.1%
ValueCountFrequency (%)
92
 
0.1%
82
 
0.1%
79
 
0.3%
622
 
0.7%
566
 
2.0%
4166
 
5.0%
3429
 
12.9%
2759
22.8%
11181
35.4%
0697
20.9%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
0
3010 
1
323 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3333
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
03010
90.3%
1323
 
9.7%

Length

2022-11-05T22:56:44.867212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-05T22:56:45.155443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
03010
90.3%
1323
 
9.7%

Most occurring characters

ValueCountFrequency (%)
03010
90.3%
1323
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3333
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03010
90.3%
1323
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common3333
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03010
90.3%
1323
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3333
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03010
90.3%
1323
 
9.7%

day_calls
Real number (ℝ≥0)

Distinct119
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.4356436
Minimum0
Maximum165
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-05T22:56:45.366460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median101
Q3114
95-th percentile133
Maximum165
Range165
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.06908421
Coefficient of variation (CV)0.1998203376
Kurtosis0.2431815246
Mean100.4356436
Median Absolute Deviation (MAD)13
Skewness-0.111786639
Sum334752
Variance402.7681409
MonotonicityNot monotonic
2022-11-05T22:56:45.635705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10278
 
2.3%
10575
 
2.3%
9569
 
2.1%
10769
 
2.1%
10468
 
2.0%
10867
 
2.0%
9767
 
2.0%
10666
 
2.0%
11266
 
2.0%
11066
 
2.0%
Other values (109)2642
79.3%
ValueCountFrequency (%)
02
0.1%
301
 
< 0.1%
351
 
< 0.1%
361
 
< 0.1%
402
0.1%
422
0.1%
443
0.1%
453
0.1%
472
0.1%
483
0.1%
ValueCountFrequency (%)
1651
 
< 0.1%
1631
 
< 0.1%
1601
 
< 0.1%
1583
0.1%
1571
 
< 0.1%
1561
 
< 0.1%
1521
 
< 0.1%
1515
0.2%
1506
0.2%
1491
 
< 0.1%

day_charge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1667
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.56230723
Minimum0
Maximum59.64
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-05T22:56:45.944732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.288
Q124.43
median30.5
Q336.79
95-th percentile46.028
Maximum59.64
Range59.64
Interquartile range (IQR)12.36

Descriptive statistics

Standard deviation9.259434554
Coefficient of variation (CV)0.3029690947
Kurtosis-0.01981178724
Mean30.56230723
Median Absolute Deviation (MAD)6.17
Skewness-0.02908326834
Sum101864.17
Variance85.73712826
MonotonicityNot monotonic
2022-11-05T22:56:46.381505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.188
 
0.2%
27.128
 
0.2%
29.678
 
0.2%
31.187
 
0.2%
29.827
 
0.2%
27.597
 
0.2%
30.386
 
0.2%
33.126
 
0.2%
32.186
 
0.2%
24.876
 
0.2%
Other values (1657)3264
97.9%
ValueCountFrequency (%)
02
0.1%
0.441
< 0.1%
1.331
< 0.1%
1.341
< 0.1%
2.131
< 0.1%
2.991
< 0.1%
3.211
< 0.1%
3.321
< 0.1%
4.41
< 0.1%
4.591
< 0.1%
ValueCountFrequency (%)
59.641
< 0.1%
58.961
< 0.1%
58.71
< 0.1%
57.361
< 0.1%
57.041
< 0.1%
56.831
< 0.1%
56.591
< 0.1%
56.071
< 0.1%
55.781
< 0.1%
55.511
< 0.1%

evening_calls
Real number (ℝ≥0)

Distinct123
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.1143114
Minimum0
Maximum170
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-05T22:56:47.443022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3114
95-th percentile133
Maximum170
Range170
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.92262529
Coefficient of variation (CV)0.1989987746
Kurtosis0.206156468
Mean100.1143114
Median Absolute Deviation (MAD)13
Skewness-0.05556313904
Sum333681
Variance396.9109986
MonotonicityNot monotonic
2022-11-05T22:56:47.845557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10580
 
2.4%
9479
 
2.4%
10871
 
2.1%
10270
 
2.1%
9770
 
2.1%
8869
 
2.1%
10168
 
2.0%
10967
 
2.0%
9866
 
2.0%
11165
 
2.0%
Other values (113)2628
78.8%
ValueCountFrequency (%)
01
 
< 0.1%
121
 
< 0.1%
361
 
< 0.1%
371
 
< 0.1%
421
 
< 0.1%
431
 
< 0.1%
441
 
< 0.1%
451
 
< 0.1%
463
0.1%
486
0.2%
ValueCountFrequency (%)
1701
 
< 0.1%
1681
 
< 0.1%
1641
 
< 0.1%
1591
 
< 0.1%
1571
 
< 0.1%
1561
 
< 0.1%
1553
0.1%
1542
 
0.1%
1531
 
< 0.1%
1526
0.2%

evening_charge
Real number (ℝ≥0)

Distinct1440
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.08354035
Minimum0
Maximum30.91
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-05T22:56:48.158867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.1
Q114.16
median17.12
Q320
95-th percentile24.17
Maximum30.91
Range30.91
Interquartile range (IQR)5.84

Descriptive statistics

Standard deviation4.310667643
Coefficient of variation (CV)0.2523287067
Kurtosis0.02548740481
Mean17.08354035
Median Absolute Deviation (MAD)2.92
Skewness-0.02385798901
Sum56939.44
Variance18.58185553
MonotonicityNot monotonic
2022-11-05T22:56:48.434543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.2511
 
0.3%
16.1211
 
0.3%
15.910
 
0.3%
17.099
 
0.3%
18.629
 
0.3%
17.999
 
0.3%
14.449
 
0.3%
18.968
 
0.2%
16.358
 
0.2%
16.978
 
0.2%
Other values (1430)3241
97.2%
ValueCountFrequency (%)
01
< 0.1%
2.651
< 0.1%
3.591
< 0.1%
3.611
< 0.1%
3.731
< 0.1%
4.091
< 0.1%
4.181
< 0.1%
4.51
< 0.1%
4.761
< 0.1%
4.981
< 0.1%
ValueCountFrequency (%)
30.911
< 0.1%
30.751
< 0.1%
30.111
< 0.1%
29.891
< 0.1%
29.831
< 0.1%
29.791
< 0.1%
29.621
< 0.1%
29.521
< 0.1%
29.011
< 0.1%
28.891
< 0.1%

night_calls
Real number (ℝ≥0)

Distinct120
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.1077108
Minimum33
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-05T22:56:48.840976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile68
Q187
median100
Q3113
95-th percentile132
Maximum175
Range142
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.56860935
Coefficient of variation (CV)0.1954755452
Kurtosis-0.07201957894
Mean100.1077108
Median Absolute Deviation (MAD)13
Skewness0.03249957015
Sum333659
Variance382.9304717
MonotonicityNot monotonic
2022-11-05T22:56:49.919981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10584
 
2.5%
10478
 
2.3%
9176
 
2.3%
10272
 
2.2%
10069
 
2.1%
10669
 
2.1%
9867
 
2.0%
9466
 
2.0%
10365
 
2.0%
9564
 
1.9%
Other values (110)2623
78.7%
ValueCountFrequency (%)
331
< 0.1%
361
< 0.1%
381
< 0.1%
422
0.1%
441
< 0.1%
461
< 0.1%
481
< 0.1%
492
0.1%
502
0.1%
512
0.1%
ValueCountFrequency (%)
1751
 
< 0.1%
1661
 
< 0.1%
1641
 
< 0.1%
1581
 
< 0.1%
1572
0.1%
1562
0.1%
1552
0.1%
1542
0.1%
1533
0.1%
1523
0.1%

night_charge
Real number (ℝ≥0)

Distinct933
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.039324932
Minimum1.04
Maximum17.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-05T22:56:50.437688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile5.316
Q17.52
median9.05
Q310.59
95-th percentile12.73
Maximum17.77
Range16.73
Interquartile range (IQR)3.07

Descriptive statistics

Standard deviation2.275872838
Coefficient of variation (CV)0.2517746463
Kurtosis0.08566317984
Mean9.039324932
Median Absolute Deviation (MAD)1.54
Skewness0.008886236769
Sum30128.07
Variance5.179597173
MonotonicityNot monotonic
2022-11-05T22:56:50.857326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.6615
 
0.5%
9.4515
 
0.5%
8.4714
 
0.4%
8.8814
 
0.4%
7.6913
 
0.4%
8.6412
 
0.4%
10.811
 
0.3%
10.4911
 
0.3%
10.3511
 
0.3%
8.5711
 
0.3%
Other values (923)3206
96.2%
ValueCountFrequency (%)
1.041
< 0.1%
1.971
< 0.1%
2.031
< 0.1%
2.131
< 0.1%
2.252
0.1%
2.41
< 0.1%
2.431
< 0.1%
2.451
< 0.1%
2.551
< 0.1%
2.591
< 0.1%
ValueCountFrequency (%)
17.771
< 0.1%
17.191
< 0.1%
16.991
< 0.1%
16.551
< 0.1%
16.421
< 0.1%
16.391
< 0.1%
15.971
< 0.1%
15.861
< 0.1%
15.851
< 0.1%
15.761
< 0.1%

international_calls
Real number (ℝ≥0)

Distinct21
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.479447945
Minimum0
Maximum20
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-05T22:56:51.239353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.461214271
Coefficient of variation (CV)0.5494458917
Kurtosis3.083588982
Mean4.479447945
Median Absolute Deviation (MAD)1
Skewness1.321478166
Sum14930
Variance6.057575686
MonotonicityNot monotonic
2022-11-05T22:56:51.481033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3668
20.0%
4619
18.6%
2489
14.7%
5472
14.2%
6336
10.1%
7218
 
6.5%
1160
 
4.8%
8116
 
3.5%
9109
 
3.3%
1050
 
1.5%
Other values (11)96
 
2.9%
ValueCountFrequency (%)
018
 
0.5%
1160
 
4.8%
2489
14.7%
3668
20.0%
4619
18.6%
5472
14.2%
6336
10.1%
7218
 
6.5%
8116
 
3.5%
9109
 
3.3%
ValueCountFrequency (%)
201
 
< 0.1%
191
 
< 0.1%
183
 
0.1%
171
 
< 0.1%
162
 
0.1%
157
 
0.2%
146
 
0.2%
1314
0.4%
1215
0.5%
1128
0.8%

international_charge
Real number (ℝ≥0)

Distinct162
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.764581458
Minimum0
Maximum5.4
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-05T22:56:51.868057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.54
Q12.3
median2.78
Q33.27
95-th percentile3.97
Maximum5.4
Range5.4
Interquartile range (IQR)0.97

Descriptive statistics

Standard deviation0.7537726127
Coefficient of variation (CV)0.2726534284
Kurtosis0.6096104298
Mean2.764581458
Median Absolute Deviation (MAD)0.48
Skewness-0.2452865083
Sum9214.35
Variance0.5681731516
MonotonicityNot monotonic
2022-11-05T22:56:52.200732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.762
 
1.9%
3.0559
 
1.8%
2.6556
 
1.7%
2.9456
 
1.7%
2.7353
 
1.6%
2.8653
 
1.6%
2.7553
 
1.6%
2.9752
 
1.6%
352
 
1.6%
2.6251
 
1.5%
Other values (152)2786
83.6%
ValueCountFrequency (%)
018
0.5%
0.31
 
< 0.1%
0.351
 
< 0.1%
0.542
 
0.1%
0.572
 
0.1%
0.591
 
< 0.1%
0.651
 
< 0.1%
0.681
 
< 0.1%
0.71
 
< 0.1%
0.731
 
< 0.1%
ValueCountFrequency (%)
5.41
 
< 0.1%
5.11
 
< 0.1%
4.971
 
< 0.1%
4.941
 
< 0.1%
4.912
0.1%
4.863
0.1%
4.831
 
< 0.1%
4.812
0.1%
4.752
0.1%
4.733
0.1%

total_charge
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2227
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.44975398
Minimum22.93
Maximum96.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2022-11-05T22:56:52.632070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum22.93
5-th percentile42.338
Q152.38
median59.47
Q366.48
95-th percentile76.516
Maximum96.15
Range73.22
Interquartile range (IQR)14.1

Descriptive statistics

Standard deviation10.50226075
Coefficient of variation (CV)0.1766577664
Kurtosis0.04789313179
Mean59.44975398
Median Absolute Deviation (MAD)7.03
Skewness-0.03479132737
Sum198146.03
Variance110.2974809
MonotonicityNot monotonic
2022-11-05T22:56:53.066749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55.056
 
0.2%
63.565
 
0.2%
52.985
 
0.2%
64.885
 
0.2%
58.315
 
0.2%
63.435
 
0.2%
58.035
 
0.2%
52.55
 
0.2%
67.255
 
0.2%
65.255
 
0.2%
Other values (2217)3282
98.5%
ValueCountFrequency (%)
22.931
< 0.1%
23.251
< 0.1%
25.521
< 0.1%
25.871
< 0.1%
27.021
< 0.1%
27.081
< 0.1%
27.541
< 0.1%
27.771
< 0.1%
28.731
< 0.1%
30.041
< 0.1%
ValueCountFrequency (%)
96.151
< 0.1%
92.291
< 0.1%
92.21
< 0.1%
90.461
< 0.1%
90.121
< 0.1%
89.761
< 0.1%
89.311
< 0.1%
88.971
< 0.1%
88.661
< 0.1%
88.391
< 0.1%

churn
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
0
2850 
1
483 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3333
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02850
85.5%
1483
 
14.5%

Length

2022-11-05T22:56:54.599256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-05T22:56:54.872277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
02850
85.5%
1483
 
14.5%

Most occurring characters

ValueCountFrequency (%)
02850
85.5%
1483
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3333
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02850
85.5%
1483
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
Common3333
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02850
85.5%
1483
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3333
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02850
85.5%
1483
 
14.5%

Interactions

2022-11-05T22:56:36.248189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:55:54.792090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:55:59.028245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:02.096705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:05.439105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:08.690535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:12.427249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:18.235110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:21.661860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:24.787190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:27.943977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:32.773406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:36.585934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:55:55.206085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:55:59.288103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:02.334723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:05.637986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:08.891211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-05T22:56:21.897114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:25.010376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:28.206413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:33.030124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:36.900621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-05T22:56:02.813924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-05T22:56:15.137877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:19.695422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:22.728191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:25.778435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:29.719522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-05T22:56:09.858547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:15.914598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:20.145457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:23.218418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-05T22:56:30.360570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-05T22:56:38.948085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:55:57.329172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-05T22:56:34.703807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:39.255108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:55:57.669448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:01.161774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:04.331562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:07.720808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-05T22:56:26.768707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:31.316641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:34.946493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:39.603816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:55:58.033303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:01.366451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:04.647290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:07.999830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:11.172614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:17.047708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:20.882509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:23.978296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:26.998547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:31.669667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:35.238208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:39.983672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:55:58.384976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:01.623500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:04.934973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:08.257849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:11.638872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:17.435883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:21.137528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:24.267146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:27.378768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:32.095355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:35.614819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:40.285368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:55:58.724544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:01.860691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:05.177993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:08.478871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:12.021524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:17.746722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:21.421527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:24.493166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:27.694962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:32.536387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-05T22:56:35.919843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-11-05T22:56:55.062291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-05T22:56:55.513323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-05T22:56:55.971412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-05T22:56:56.371442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-05T22:56:56.687137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-05T22:56:56.921072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-05T22:56:40.856408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-05T22:56:41.604469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

account_lengthvoice_mail_messagescustomer_service_callsinternational_planday_callsday_chargeevening_callsevening_chargenight_callsnight_chargeinternational_callsinternational_chargetotal_chargechurn
0128251011045.079916.789111.0132.7075.560
1107261012327.4710316.6210311.4533.7059.240
213700011441.3811010.301047.3253.2962.290
3840217150.90885.26898.8671.7866.800
47503111328.3412212.611218.4132.7352.090
51180019837.9810118.751189.1861.7067.610
612124308837.0910829.621189.5772.0378.310
71470017926.69948.76969.5361.9246.900
81170109731.378029.89909.7142.3573.320
914137018443.9611118.879714.6953.0280.540

Last rows

account_lengthvoice_mail_messagescustomer_service_callsinternational_planday_callsday_chargeevening_callsevening_chargenight_callsnight_chargeinternational_callsinternational_chargetotal_chargechurn
332311705012620.139721.195610.2233.6755.211
332415901011428.8710516.80828.7243.1357.520
3325780209932.88889.9410910.9542.5156.280
33269601012818.128724.21928.0574.0254.400
3327790209822.906816.121289.9653.1952.170
332819236207726.5512618.328312.5662.6760.100
3329680305739.295513.041238.6142.5963.530
33302802010930.745824.55918.6463.8167.740
333118402110536.358413.571376.26101.3557.530
333274250011339.858222.607710.8643.7077.010